Water quality prediction based on a novel hybrid model of ARIMA and RBF neural network
文献类型：会议论文
| 作者 | Deng, Weihui; Wang, Guoyin; Zhang, Xuerui ; Guo, Yishuai; Li, Guangdi
|
| 出版日期 | 2014 |
| 会议日期 | November 27, 2014 - November 29, 2014 |
| 会议地点 | Shenzhen, China |
| DOI | 10.1109/CCIS.2014.7175699 |
| 页码 | 33-40 |
| 英文摘要 | Improving the accuracy of the water quality prediction is an important and difficult task facing decision makers in water resources management. Many researchers have argued that combining different models can be an effective way of improving upon their predictive performance. The hybrid models of autoregressive integrated moving average (ARIMA) and neural network, as one of the most popular hybrid models for time series forecasting, have recently been shown successfully for water quality prediction. However, these models have many assumptions and limitations. In this paper, a novel hybrid model of ARIMA and Radial Basis Function Neural Network (RBF-NN) is proposed in order to yield more general and higher accuracy prediction model than traditional hybrid ARIMA-ANNs models for water quality prediction. The proposed model consist of an ARIMA model, which was a linear model and used to obtain the existing linear structures, and an RBF-NN model that is used to capture the nonlinear architectures and do the prediction. Experiments results show that the proposed model can be an available and effective way to improve the accuracy of the water quality prediction. © 2014 IEEE. |
| 会议录 | 3rd IEEE International Conference on Cloud Computing and Intelligence Systems, CCIS 2014
 |
| 语种 | 英语 |
| 源URL | [http://119.78.100.138/handle/2HOD01W0/4768]  |
| 专题 | 大数据挖掘及应用中心 |
| 作者单位 | Chongqing Institute of Green and Intelligent Technology, Chinese Academy of Sciences, Chongqing; 400714, China |
| 推荐引用方式 GB/T 7714 | Deng, Weihui,Wang, Guoyin,Zhang, Xuerui,et al. Water quality prediction based on a novel hybrid model of ARIMA and RBF neural network[C]. 见:. Shenzhen, China. November 27, 2014 - November 29, 2014. |
入库方式： OAI收割
来源：重庆绿色智能技术研究院
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。